High proportions of nonpathogenic Streptomyces are associated with common scab-resistant potato lines and less severe disease
Bibliographic record
Abstract
Streptomyces isolates were obtained from potato tubers with common scab lesions from 2 fields over a 3 year period in Minnesota and a 5 year period in Maine. Isolates were obtained from different potato cultivars or breeding lines and types of scab lesions. A majority of isolates could be classified as putative pathogens based on the presence of genes for biosynthesis of the pathogenicity determinant, thaxtomin, but large numbers of streptomycetes lacking genes for thaxtomin biosynthesis (presumably nonpathogenic) were also recovered. Most Streptomyces isolates recovered from raised and pitted lesions were pathogens, whereas mostly nonpathogenic isolates were recovered from unblemished potato skin or nonscab lesions. Fewer pathogenic than nonpathogenic isolates were recovered from the most resistant potato lines. The proportion and diversity of nonpathogenic isolates recovered was higher in Maine than in Minnesota. The association between greater numbers of nonpathogenic Streptomyces and less severe common scab suggests that the interaction between plant genotype and Streptomyces microbial community is important in determining the severity of common scab on potato, and emphasizes the role of complex interactions between plants and microbial populations on and near plant roots in plant disease outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".